Assigning a new problem record based on a similarity to previous problem records

ABSTRACT

Aspects of the invention include receiving a new problem record. A corpus of existing problem records that were previously assigned to problem record owners and grouped into documents based on their assigned problem record owners is accessed. Each document in the corpus has an assigned problem record owner. A document in the corpus that is most similar to the new problem record is identified. The identifying includes comparing text in the new problem record to text in the documents. The new problem record is assigned to the problem record owner that is assigned to the identified document.

BACKGROUND

The present invention generally relates to assigning problem records,and more specifically, to assigning a new problem record based on asimilarity to previous problem records.

The testing and debugging of complex systems often take long periods oftime and can lead to the identification of a large number of problemsthat may require design modifications and other changes to the system inorder to correct them. Innovations that can reduce the number ofproblems or speed problem resolution are desired since they will speeddelivery of systems to customers. Computer systems are one example of acomplex system composed of large numbers of interacting hardware,firmware, and software components that work together. As system problemsare detected in test, it is important to try to assign the problem tothe design team for the component that needs to be modified in order tofix the detected problem. This however is no simple matter, especiallywhen there are a large number of components. Accurate problem assignmentcan be challenging, and inaccurate assignments result in delays inresolving problems.

SUMMARY

Embodiments of the present invention include assigning new problemrecords based on a similarity to previous problem records. Anon-limiting example computer-implemented method includes receiving anew problem record. A corpus of existing problem records that werepreviously assigned to problem record owners and grouped into documentsbased on their assigned problem record owners is accessed. Each documentin the corpus has an assigned problem record owner. A document in thecorpus that is most similar to the new problem record is identified. Theidentifying includes comparing text in the new problem record to text inthe documents. The new problem record is assigned to the problem recordowner that is assigned to the identified document.

Other embodiments of the present invention implement features of theabove-described method in computer systems and computer programproducts.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a block diagram of a system for assigning new problemrecords according to one or more embodiments of the present invention;

FIG. 2 depicts a process flow diagram of a method for generating acorpus of existing problem records according to one or more embodimentsof the present invention;

FIG. 3 depicts a process flow diagram of a method for assigning a newproblem record according to one or more embodiments of the presentinvention;

FIG. 4 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 5 depicts abstraction model layers according to one or moreembodiments of the present invention; and

FIG. 6 illustrates a system for buffer overflow trapping according toone or more embodiments of the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagrams, or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled”, and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

DETAILED DESCRIPTION

One or more embodiments of the present invention assign new problemrecords to a person or group of people for problem resolution. Inaccordance with one or more embodiments of the present invention, theassigning is performed based on a similarity of new problem records topreviously assigned problem records. Assigning new problem records basedon a similarity to previously assigned problem records improves problemrecord assignment accuracy, which reduces test time and field problemresolution time. One or more embodiments of the present invention can beused to automatically assign new problem records, to verify problemrecord assignments, and/or to identify features of previouslymisclassified problem records. Future incorrect component/ownerassignments can be avoided by analyzing existing problem records thatwere originally incorrectly assigned and using this information forfuture problem record assignments.

When testers of a computer system detect a problem with the system theywill often write a description of the problem and collect logs, tracesand other data about the problem. The testers will then try to assigneach detected problem to the component team they feel will most likelybe able to fix the problem, but even the most experienced testers willnot always accurately assign all of the problems that are detected tothe correct component team. Problem records can include information suchas, but not limited to: a title of the problem record, a date that theproblem record was opened, an assigned owner of the problem record, aseverity of the problem, a summary of the problem, a component(s) orproduct(s) causing the problem, a lifecycle phase (e.g., unit test,production in the field, etc.) when the problem was opened, and commentsabout the problem. All or a subset of this information can be in theproblem record as unstructured text.

It can be difficult to classify or categorize an isolated piece of text,such as a problem description, based just on the content of the text.This is especially true for complex systems, such as those with asignificant number of interacting components under test or experiencingproblems in the field. Depending on what category the text falls in,different actions might be taken. For example, in software defecttracking a problem ticket is opened to describe and track a softwaredefect, but often the creator of the problem ticket (e.g., the tester,field support coordinator, etc.) does not know or cannot be certain ofwhat component(s) or product(s) is causing the defect. This can make itdifficult assign the software defect to the correct problem owner forproblem resolution. In this example, the problem description in theproblem ticket is the text needing to be categorized, and the categoryis problem owner (e.g., person or group of people) that should beassigned the defect.

It is possible to set predefined classification rules for the incomingproblem records, but this approach alone is unlikely to make asubstantial improvement in first assignment accuracy especially forcomplex systems with often changing assignment criteria.

In accordance with one or more embodiments of the present invention,assignment accuracy is improved by not simply analyzing the isolatedproblem description but by also comparing it with the text of previouscorrectly classified documents to categorize incoming data. In thesoftware defect example, when a new problem ticket is opened, the textof this new problem ticket is compared to text in previous problemtickets to identify those that are most similar. Comparing the text ofthe new document with the text of old documents results in a moreaccurate assessment of which category the new document most likelybelongs in.

Contemporary methods of classifying text include the bag-of-words modelwhich uses an order-less representation of text that only considers thecounts of words in the text. Another contemporary method of classifyingtext includes the use of an N-gram model which allows for theconsideration of groupings of words but is more suited to predicting thenext item in a series. Regular expressions can also be used to identifypatterns in the text, but this requires pre-defining patterns and maynot be accurate when text is worded differently.

One or more embodiments of the present invention utilize a targetsimilarity-difference gradient method on combined pieces of text tocategorize a new document (e.g., a new problem record). The targetsimilarity-difference gradient method can be used to identify the mostsimilar documents (e.g., those containing the most similar problemrecords) in a corpus relative to a target (e.g., a new problem record)which can be a document, a record, or a string of text. A targetsimilarity calculation can be used to identify relevant content in acorpus of documents or records. In accordance with one or moreembodiments, a term frequency (TF) list is created for one or moredocuments of a corpus. An inverse document frequency (IDF) is calculatedfor each listed term, and a TFIDF is calculated for each listed term. Asimilarity ranking for one or more documents of the corpus is determinedusing a target similarity calculation based on the TFIDF for each listedterm. Additionally, a difference gradient portion can also be utilizedto locate and isolate similarities in documents as well as to furtheridentify and locate distinctions between the identified similardocuments. Accordingly, the relevance of records and documents in acorpus in light of a target document or record (e.g., a new problemrecord) can be determined in a simplified manner that reducescomputational requirements.

Accurate problem assignment can be challenging, and inaccurateassignments can result in significant delays in resolving problems. Forexample, it is not uncommon, during system testing, for a complex systemto have a thousand problems that have been identified and that need tobe addressed. Assuming that there are one thousand problem records, andthat ten percent of the problem records are assigned incorrectly, andthat it takes two days for the group to whom each problem record isincorrectly assigned to do the initial analysis of the failure and thenattempt to funnel the problem record to the correct problem owner forproblem resolution, incorrect problem assignment adds two hundred daysto the problem resolution time of the one thousand problem records.

One or more embodiments of the present invention can be utilized fortesting as well as for supporting problem records describing problemsencountered in the field (e.g., at customer locations that are utilizingthe computer systems) where delays in resolution time can directlyimpact customer satisfaction and potential future sales.

One or more embodiments of the present invention provide automaticcategorization of problems based on the failing component, or problemowner, which can result in higher accuracy in problem record assignment.In addition, less human time is wasted investigating an issue, orproblem, in order to determine which team to send it to.

The problem record assignment described herein utilizes a corpus ofpreviously assigned problem records and does not require any trainingprocesses or building of a machine learning model. The problem recordowners of previously assigned problem records can be assumed to becorrect, for example, when the problem record is closed and/or when theassigned problem record owner accepts the assigned problem record.

Turning now to FIG. 1, a block diagram of components of a system 100 forassigning new problem records is generally shown in accordance with oneor more embodiments of the present invention. The components shown inFIG. 1 include a corpus 104 of documents 106, a program recordassignment module 102, a new problem record 108 to be assigned, and anassignment of the new problem record 110. In accordance with one or moreembodiments of the present invention, the components shown in FIG. 1 maybe located on the same computer processor. In accordance with one ormore other embodiments of the present invention, all or a subset of thecomponents are located on different computer processors and/or memorydevices that communicate with each other via a computer network such asnetwork 665 of FIG. 6 below.

As shown in FIG. 1, each document 106 includes one or more problemrecords that have been categorized based on the problem record ownerthat has been assigned to the problem record. As shown in FIG. 1, all ofthe existing problem records in each of the documents 106 have beenassigned to the same problem record owner. Existing problem recordsassigned to problem record owner 1 are in category 1 and stored togetherin the same document 106, existing problem records assigned to problemrecord owner 2 are in category 2 and stored together in the samedocument 106, and existing problem records assigned to problem recordowner x are in category x and stored together in the same document 106.An embodiment of a method where existing problem records arecategorized, or grouped, is described below in reference to FIG. 2. Thecorpus 104 can be stored in any manner known in the art including, butnot limited to, in a database or in a storage record. In addition, thecorpus 104 can be stored on any type of memory or storage devices knownin the art.

As shown in FIG. 1, a new problem record 108 is input to the problemrecord assignment module 102, and the problem record assignment module102 outputs a suggested assignment of the new problem record 110. Thenew problem record 108 and the assignment of the new problem record 110can be stored in any manner known in the art including, but not limitedto, in a database or in storage record. In addition, the new problemrecord 110 and the assignment of the new problem record 110 can bestored on any type of memory or storage devices known in the art. Theprocessing performed by the problem record assignment module 102executing on a computer processor to generate an assignment of the newproblem record 110 is described below in reference to FIG. 3.

It is to be understood that the block diagram of FIG. 1 is not intendedto indicate that the system 100 is to include all of the componentsshown in FIG. 1. Rather, the system 100 can include any appropriatefewer or additional components not illustrated in FIG. 1 (e.g.,additional memory components, programs, functional blocks, connectionsbetween functional blocks, modules, inputs, outputs, etc.). For example,the problem record assignment module 102 may also generate the corpus104 as well as perform the assignment of the new problem record 110. Inother embodiments, these functions may be performed by separate modulesoperating on the same or different computer processors. Further, theembodiments described herein with respect to system 100 may beimplemented with any appropriate logic, wherein the logic, as referredto herein, can include any suitable hardware (e.g., a processor, anembedded controller, or an application specific integrated circuit,among others), software (e.g., an application, among others), firmware,or any suitable combination of hardware, software, and firmware, invarious embodiments.

Turning now to FIG. 2, a process flow diagram of a method 200 forgenerating a corpus of existing problem records is generally shownaccording to one or more embodiments of the present invention. Theprocessing shown in FIG. 2 may be executed by a standalone computerprocessor and/or by a node in a cloud, such as node 10 in FIG. 4 below.In one or more embodiments of the present invention, the processingshown in FIG. 2 is performed by problem record assignment module 102 ofFIG. 1.

At block 202, existing problem records that were previously assigned toproblem record owners are grouped based on their currently assignedproblem record owners. Each group of program records includes existingproblem records that are currently assigned to the same problem recordowner. The assigned problem record owner is the person (or department orteam) that resolved, or fixed, the problem described in the problemrecord. For problem records that were originally assigned to anincorrect problem record owner, the assigned problem record owner may beupdated once the correct problem record owner is identified. Inaccordance with one or more embodiments of the present invention, theproblem records may be further partitioned based on a time that theywere opened (e.g., in the previous 90 days, in the previous 91-180 days,more than 180 days ago) and/or a lifecycle phase (e.g., unit testing,integration testing, production, etc.) when a problem record was opened.By grouping documents based on both problem record owner and a time thatthe problem record was opened, current trends in problem recordassignment can be given more weight than older trends when assigning newproblem records. By grouping documents based on both problem recordowner and lifecycle phase, differences in problem record owners atdifferent phases can be taken into account when assigning new problemrecords.

At block 204 of FIG. 2, a corpus of documents, such as corpus 104 ofFIG. 1, is created by merging, or appending, the problem records in eachgroup into a single document, such as documents 106 of FIG. 1. Eachgroup has an assigned problem record owner. At block 206, the termfrequencies (TFs) for the words, or terms, in each document arecalculated. In accordance with one or more embodiments of the presentinvention, the TFs are calculated for only a subset of the words in eachdocument. Words may be filtered out based, for example, on parts ofspeech (e.g., conjunctions are removed, stop words are removed) and/or acustom set of rules (e.g., a customized set of stop words for atechnical document). Stemming or lemmatization of the remaining words inthe document may be performed on the remaining words in the document tocombine different forms of the same word.

One or more embodiments of the present invention utilize a form of termweighting known as the Luhn Assumption where the weight of a term thatoccurs in a document is proportional to the term frequency (TF). TF isthe number of occurrences of each word, or term, in the record ordocument. One or more embodiments of the present invention utilize anormalized TF so as to not give higher weighting to a larger document.This can be calculated by dividing the number of times a term appears inthe document by the sum of all TFs. This calculation produces a decimalfraction between 0 and 1. If the term does not appear at all in thedocument, the normalized TF of the term is zero. If the term is the onlyterm in the document, then the normalized TF is one. Thus, the moreprevalent the term is in the document, the closer the normalized TF willbe to one.

At block 208, an inverse document frequency (IDF) is calculated for eachword (or a subset of the words after filtering, stemming and/orlemmatization) in the corpus. The IDF can be calculated by taking thelog of the total number of documents in the corpus divided by the numberof documents in which the term appears. The IDF is used to give moreweight to rarer terms in the corpus. The IDF for any term is the sameacross the entire corpus. For example, consider the case where everydocument contains one of the input terms. In this case, this input termis of little value in helping zero-in on the most relevant documents.When the term appears in all documents, the IDF is equal to zero. Therarer the term across the corpus, the closer the value of the IDF willbe to the order of magnitude of the number of documents in the corpus.For example, if there are 1,000 documents and only one contains theinput term, the IDF would be equal to three, and if there were 100,000documents and only one contains the input term, the IDF is equal tofive.

At block 210 of FIG. 2, each TF and its corresponding IDF for words inthe document are multiplied (resulting in TFIDFs) to give more weight,or importance, to words that occur less frequently in the corpus andless weight to words in the document that occur more frequently in thecorpus. At block 212, the TFIDF values are stored for each document inthe corpus. In accordance with one or more embodiments of the presentinvention, the TFIDF values for each document are stored as ann-dimensional vector of TFIDF, where n is the number of words beinganalyzed for the document after the filtering described above.

The process flow diagram of FIG. 2 is not intended to indicate that theoperations of the method 200 are to be executed in any particular order,or that all of the operations of the method 200 are to be included inevery case. Additionally, the method 200 can include any suitable numberof additional operations.

Turning now to FIG. 3, a process flow diagram of a method 300 forassigning a new problem record is generally shown according to one ormore embodiments of the present invention. The processing shown in FIG.3 may be executed by a standalone computer processor and/or by a node ina cloud, such as node 10 in FIG. 4 below. In one or more embodiments ofthe present invention, the processing shown in FIG. 2 is performed byproblem record assignment module 102 of FIG. 1.

At block 302, a new problem record, such as new problem record 108 ofFIG. 1, is received. At block 304, the TFs for the words, or terms, inthe new problem record are calculated. In accordance with one or moreembodiments of the present invention, the TFs are calculated for only asubset of the words in the new problem record. Words may be filtered outas described above with reference to block 206 of FIG. 2. Stemming orlemmatization of the remaining words in the new problem record may beperformed on the remaining words in the new problem record to combinedifferent forms of the same word. At block 306, IDFs for each of thewords (or a subset of the words after filtering, stemming and/orlemmatization) in the new problem record are determined. The IDFs thatwere calculated when the documents 106 in the corpus 104 were createdcan be accessed at block 306. In accordance with one or more embodimentsof the present invention, the IDFs are stored in the corpus. The IDFsare used to determine how rare each of the words are in the corpus ofexisting problem records.

At block 308, the product of the TF and IDF (TFIDF) is calculated foreach term (or a subset of the words after filtering, stemming and/orlemmatization) in the text of the new product record. This produces ann-dimensional vector of TFIDF weights for the new product record where nis equal to the number of terms in the new document. As known in theart, a vector is a list of terms in a row or a column that in some casesmay be a subsection of a larger matrix of terms.

At block 310, the TFIDFs of the new problem record are compared to theTFIDFs of the documents in the corpus to determine which document(s) inthe corpus is most similar to the new problem record. In accordance withone or more embodiments of the present invention, this includescalculating the dot-product or cosine similarity between then-dimensional input TFIDF vector for the new problem record and theTFIDF vector for each document in the corpus.

In accordance with one or more embodiments, the TFIDF for each inputterm (the number of input terms is “n”) in the target document (e.g.,new problem record) is multiplied by the TFIDF for the same term in eachdocument in the corpus to create a similarity vector. The product foreach of the similarity vector terms is summed, and the total is dividedby the resultant for each of the n-dimensional vectors. This essentiallynormalized value represents an overall similarity score of each corpusdocument relative to the new problem record. Each term in the newproblem record is multiplied to itself and since each TFIDF value foreach term will match exactly since they are identical, the similarityscore will almost always have the highest value since it is a perfectmatch. Each resultant (i.e., each n-dimensional vector) is calculatedusing the square root of the sum of the square of each element (ordimension) of the vector.

For “cosine similarity” the “target resultant” is used for the newproblem record and the resultant for each document in the corpus is usedfor each document. The target resultant is the square root of the sum ofthe squares for the TFIDF values for the document vector of interest.Dividing by the resultant effectively normalizes the value of each termTFIDF value.

For “target similarity” the resultant is used for both the new problemrecord and the corpus documents since in almost all cases the newproblem record resultant will be larger than the resultant for eachcorpus document since the corpus document term will be zero if there isnot a corresponding term in the corpus document. In this case, the TFIDFvalue of each word in the target document (e.g., the new problem record)is multiplied by the corresponding TFIDF value for each same word foreach document in the corpus one at a time. The resultant in this case isthe square root of the sum of the squares for the vector created bymultiplying the corresponding word TFIDF together. The target similaritytechnique simplifies the calculations because the resultant for eachdocument in the corpus no longer needs to be calculated.

The document with the highest score is considered the one with thegreatest relevance to the new problem record. For example, if thedocument does not contain an input term from the new problem record, theTFIDF product of the new problem record term and the corpus documentterm equals zero. Documents that have a large number of rare input termsfrom the new problem record relative to documents in the rest of thecorpus will have a higher TFIDF value for that input term. Again, thedocument with the highest score is weighted as the document that bestcorresponds to the similarity query.

At block 312, an owner is assigned to the new problem record based onthe results of the comparing at block 310. The problem record ownerassigned to the new problem record is the problem record owner of theexisting problem record(s) in the document deemed to be most similar tothe new problem record.

In accordance with one or more embodiments of the present invention,once the new problem record is assigned to a problem record owner, thenew problem record is automatically transferred to the problem recordowner for problem resolution.

In accordance with one or more embodiments of the present invention,where a person (e.g., a tester) controls the transfer of new problemrecords to the problem record owner, the assignment is sent to theperson and the person uses the assigned problem record owner from thesystem as a suggestion of who should be responsible for fixing theproblem. The person can override the problem record owner assigned bythe system.

In accordance with one or more embodiments of the present invention,where a person (e.g., a tester) inputs a suggestion for the problemrecord owner, the assigned problem record owner is compared to thesuggested problem record owner input by the person. If the two match,the new problem record is automatically transferred to the assignedproblem record owner for problem resolution. If the two do not match,then the assignment is sent to the person for use in determining whoshould be responsible for fixing the problem. The person can overridethe problem record owner assigned by the system.

In accordance with one or more embodiments of the present invention,future incorrect owner assignments can be avoided by analyzing existingproblem records that were originally incorrectly assigned and using thisinformation for future problem record assignments.

The process flow diagram of FIG. 3 is not intended to indicate that theoperations of the method 300 are to be executed in any particular order,or that all of the operations of the method 300 are to be included inevery case. Additionally, the method 300 can include any suitable numberof additional operations.

An example of using one or more embodiments of the present invention fora software defect tracking system is described below. One skilled in theart will recognize that embodiments are not limited to software defecttracking as the techniques described herein can be applied to any systemwhere text is categorized.

In this example, a system development team has a defect trackingdatabase that is populated with defects. A defect, or problem ticket,may contain various pieces of information, such as the description ofthe problem and the component/product/team that is responsible for thepiece of software that the defect was found in. Assigning a defect tothe proper team might not be straightforward since the error might bevague or non-readable to humans (e.g., a stack trace, a memory dump,etc.). Also, it may not be clear from the problem symptoms which areathe defect should be assigned to because often more than one componentwill be involved. For example, what appears to be a computermicroprocessor problem could actually be a microprocessor code bug or anissue with computer memory or an I/O adapter. Assigning the defect tothe wrong team can lead to delays in resolving the problem.Automatically assigning the defect to the correct area will result inthe right people investigating the defect more quickly.

The existing problem tickets are separated based on the category(problem ticket owner). It is assumed that previous defects areeventually all assigned to the correct teams. This is a reasonableassumption because even if the problem ticket was opened to the wrongteam, after more thorough investigation the tickets are reassigned tothe team that fixes the problem. One or more embodiments of the presentinvention can allow for it to be more likely that the ticket is assignedto the correct team the first time.

All problem tickets for each category are merged into one large documentfor that category. The documents can be appended together to easilycreate one document with the text from all the problem tickets of thatcategory (e.g., problem owner).

Using each of the documents, a corpus is created, where each documentencompasses the text of problem tickets for a specific category. The TFfor each word in each document is calculated along with the IDF for eachword in the corpus. The product of the TF and IDF (TFIDF) for each termis calculated.

When a new defect is written up, the target similarity-differencegradient method can be used to calculate the target-similarity of thenew defect to each existing document. This will tell how closely relatedthe text from the defect is to each document in the corpus. The categoryfor the document with the highest relevance will then be used as theowner for the defect.

Text similarity can change as the test progresses in time. This can bedue to the problems associated with different components may be relatedto different areas of the design or different component functions. Oneof more embodiments of the present invention addresses the shift, ifdetected, by creating time dependent documents for performing thesimilarity analysis.

One or more embodiments of the present invention can utilize the textfrom past problem records that were assigned inaccurately initially toidentify problems that might be similar and avoid inaccurate assignmentin the future.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and problem record assignment 96.

It is understood that one or more embodiments of the present inventionare capable of being implemented in conjunction with any type ofcomputing environment now known or later developed.

Turning now to FIG. 6, a computer system for assigning new problemrecords is generally shown in accordance with one or more embodiments ofthe present invention. The methods described herein can be implementedin hardware, software (e.g., firmware), or a combination thereof. In oneor more exemplary embodiments of the present invention, the methodsdescribed herein are implemented in hardware as part of themicroprocessor of a special or general-purpose digital computer, such asa personal computer, workstation, minicomputer, or mainframe computer.The system 600 therefore may include general-purpose computer ormainframe 601 capable of running multiple instances of an O/Ssimultaneously.

In one or more exemplary embodiments of the present invention, in termsof hardware architecture, as shown in FIG. 6, the computer 601 includesone or more processors 605, memory 610 coupled to a memory controller615, and one or more input and/or output (I/O) devices 640, 645 (orperipherals) that are communicatively coupled via a local input/outputcontroller 635. The input/output controller 635 can be, for example butnot limited to, one or more buses or other wired or wirelessconnections, as is known in the art. The input/output controller 635 mayhave additional elements, which are omitted for simplicity, such ascontrollers, buffers (caches), drivers, repeaters, and receivers, toenable communications. Further, the local interface may include address,control, and/or data connections to enable appropriate communicationsamong the aforementioned components. The input/output controller 635 mayinclude a plurality of sub-channels configured to access the outputdevices 640 and 645. The sub-channels may include fiber-opticcommunications ports.

The processor 605 is a hardware device for executing software,particularly that stored in storage 620, such as cache storage, ormemory 610. The processor 605 can be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the computer 601, asemiconductor based microprocessor (in the form of a microchip or chipset), a macroprocessor, or generally any device for executinginstructions.

The memory 610 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmableread only memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), programmable read only memory (PROM), tape, compactdisc read only memory (CD-ROM), disk, diskette, cartridge, cassette orthe like, etc.). Moreover, the memory 610 may incorporate electronic,magnetic, optical, and/or other types of storage media. Note that thememory 610 can have a distributed architecture, where various componentsare situated remote from one another, but can be accessed by theprocessor 605.

The instructions in memory 610 may include one or more separateprograms, each of which comprises an ordered listing of executableinstructions for implementing logical functions. In the example of FIG.6, the instructions in the memory 610 a suitable operating system (OS)611. The operating system 611 essentially controls the execution ofother computer programs and provides scheduling, input-output control,file and data management, memory management, and communication controland related services.

In accordance with one or more embodiments of the present invention, thememory 610 may include multiple logical partitions (LPARs) each runningan instance of an operating system. The LPARs may be managed by ahypervisor, which may be a program stored in memory 610 and executed bythe processor 605.

In one or more exemplary embodiments of the present invention, aconventional keyboard 650 and mouse 655 can be coupled to theinput/output controller 635. Other output devices such as the I/Odevices 640, 645 may include input devices, for example but not limitedto a printer, a scanner, microphone, and the like. Finally, the I/Odevices 640, 645 may further include devices that communicate bothinputs and outputs, for instance but not limited to, a network interfacecard (NIC) or modulator/demodulator (for accessing other files, devices,systems, or a network), a radio frequency (RF) or other transceiver, atelephonic interface, a bridge, a router, and the like. The system 600can further include a display controller 625 coupled to a display 630.

In one or more exemplary embodiments of the present invention, thesystem 600 can further include a network interface 660 for coupling to anetwork 665. The network 665 can be an IP-based network forcommunication between the computer 601 and any external server, clientand the like via a broadband connection. The network 665 transmits andreceives data between the computer 601 and external systems. In anexemplary embodiment, network 665 can be a managed IP networkadministered by a service provider. The network 665 may be implementedin a wireless fashion, e.g., using wireless protocols and technologies,such as WiFi, WiMax, etc. The network 665 can also be a packet-switchednetwork such as a local area network, wide area network, metropolitanarea network, Internet network, or other similar type of networkenvironment. The network 665 may be a fixed wireless network, a wirelesslocal area network (LAN), a wireless wide area network (WAN) a personalarea network (PAN), a virtual private network (VPN), intranet or othersuitable network system and includes equipment for receiving andtransmitting signals. The network 665 may be implemented by the cloudcomputing environment 50 of FIG. 4.

If the computer 601 is a PC, workstation, intelligent device or thelike, the instructions in the memory 610 may further include a basicinput output system (BIOS) (omitted for simplicity). The BIOS is a setof essential software routines that initialize and test hardware atstartup, start the OS 611, and support the transfer of data among thehardware devices. The BIOS is stored in ROM so that the BIOS can beexecuted when the computer 601 is activated.

When the computer 601 is in operation, the processor 605 is configuredto execute instructions stored within the memory 610, to communicatedata to and from the memory 610, and to generally control operations ofthe computer 601 pursuant to the instructions. In accordance with one ormore embodiments of the present invention, computer 601 is an example ofa cloud computing node 10 of FIG. 4.

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

One or more of the methods described herein can be implemented with anyor a combination of the following technologies, which are each wellknown in the art: a discreet logic circuit(s) having logic gates forimplementing logic functions upon data signals, an application specificintegrated circuit (ASIC) having appropriate combinational logic gates,a programmable gate array(s) (PGA), a field programmable gate array(FPGA), etc.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

In some embodiments, various functions or acts can take place at a givenlocation and/or in connection with the operation of one or moreapparatuses or systems. In some embodiments, a portion of a givenfunction or act can be performed at a first device or location, and theremainder of the function or act can be performed at one or moreadditional devices or locations.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thepresent disclosure has been presented for purposes of illustration anddescription but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments were chosen and described in order tobest explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the steps (or operations) described thereinwithout departing from the spirit of the disclosure. For instance, theactions can be performed in a differing order or actions can be added,deleted or modified. Also, the term “coupled” describes having a signalpath between two elements and does not imply a direct connection betweenthe elements with no intervening elements/connections therebetween. Allof these variations are considered a part of the present disclosure.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include both an indirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A method comprising: receiving, by a processor, a new problem record; accessing, by the processor, a corpus of existing problem records that were previously assigned to problem record owners and grouped into documents based on their assigned problem record owners, each document having an assigned problem record owner; identifying, by the processor, a document in the corpus that is most similar to the new problem record, the identifying comprising comparing text in the new problem record to text in the documents, the comparing comprising: calculating term frequencies of words included in the new problem record; weighting each of the term frequencies of words included in the new problem record based at least in part on a number of times that the corresponding word included in the new problem record occurs in the corpus; accessing weighted term frequencies for the documents in the corpus; and comparing the weighted term frequencies of words included in the new problem record to the weighted term frequencies for the documents in the corpus, wherein the new problem record and each of the documents in the corpus are represented by a vector of terms that each include, for each of the terms, a product of a term frequency of the term and an inverse document frequency, wherein a similarity of the new problem record to each of the documents is determined based at least in part on a normalized sum of a dot product of the vectors corresponding to the new problem record and the document, and wherein the similarity is utilized to identify a document in the corpus that is most similar to the new problem record; and assigning, by the processor, the new problem record to the problem record owner that is assigned to the identified document.
 2. The method of claim 1, wherein the document that is most similar to the new problem record is the document that has weighted term frequencies that most closely align with the weighted term frequencies of words included in the new problem record.
 3. The method of claim 1, wherein at least a subset of the existing problem records were initially assigned to an incorrect problem record owner and the identifying further comprises: comparing the text in the new problem record to text in the at least a subset of the existing problem records that were initially assigned to an incorrect problem record owner.
 4. The method of claim 1, wherein the identifying further comprises utilizing a cosine similarity method to determine a document that is most similar to the new problem record.
 5. The method of claim 1, wherein the identifying utilizes a dot-product method to determine a document that is most similar to the new problem record.
 6. The method of claim 1, wherein the identifying further comprises utilizing a target similarity-difference gradient method to determine a document that is most similar to the new problem record.
 7. The method of claim 1, further comprising adding the new problem record to the document in the corpus.
 8. The method of claim 1, wherein the corpus of existing problem records are further grouped into documents based on a time that they were first opened.
 9. A system comprising: one or more processors for executing computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising: receiving a new problem record; accessing a corpus of existing problem records that were previously assigned to problem record owners and grouped into documents based on their assigned problem record owners, each document having an assigned problem record owner; identifying a document in the corpus that is most similar to the new problem record, the identifying comprising comparing text in the new problem record to text in the documents, the comparing comprising: calculating term frequencies of words included in the new problem record; weighting each of the term frequencies of words included in the new problem record based at least in part on a number of times that the corresponding word included in the new problem record occurs in the corpus; accessing weighted term frequencies for the documents in the corpus; and comparing the weighted term frequencies of words included in the new problem record to the weighted term frequencies for the documents in the corpus, wherein the new problem record and each of the documents in the corpus are represented by a vector of terms that each include, for each of the terms, a product of a term frequency of the term and an inverse document frequency, wherein a similarity of the new problem record to each of the documents is determined based at least in part on a normalized sum of a dot product of the vectors corresponding to the new problem record and the document, and wherein the similarity is utilized to identify a document in the corpus that is most similar to the new problem record; and assigning the new problem record to the problem record owner that is assigned to the identified document.
 10. The system of claim 9, wherein the document that is most similar to the new problem record is the document that has weighted term frequencies that most closely align with the weighted term frequencies of words included in the new problem record.
 11. The system of claim 9, wherein at least a subset of the existing problem records were initially assigned to an incorrect problem record owner and the identifying further comprises: comparing the text in the new problem record to text in the at least a subset of the existing problem records that were initially assigned to an incorrect problem record owner.
 12. The system of claim 9, wherein the identifying further comprises utilizing a cosine similarity method to determine a document that is most similar to the new problem record.
 13. The system of claim 9, wherein the identifying further comprises utilizing a target similarity-difference gradient method to determine a document that is most similar to the new problem record.
 14. The system of claim 9, wherein the operations further comprise adding the new problem record to the document in the corpus.
 15. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising: receiving a new problem record; accessing a corpus of existing problem records that were previously assigned to problem record owners and grouped into documents based on their assigned problem record owners, each document having an assigned problem record owner; identifying a document in the corpus that is most similar to the new problem record, the identifying comprising comparing text in the new problem record to text in the documents, the comparing comprising: calculating term frequencies of words included in the new problem record; weighting each of the term frequencies of words included in the new problem record based at least in part on a number of times that the corresponding word included in the new problem record occurs in the corpus; accessing weighted term frequencies for the documents in the corpus; and comparing the weighted term frequencies of words included in the new problem record to the weighted term frequencies for the documents in the corpus, wherein the new problem record and each of the documents in the corpus are represented by a vector of terms that each include, for each of the terms, a product of a term frequency of the term and an inverse document frequency, wherein a similarity of the new problem record to each of the documents is determined based at least in part on a normalized sum of a dot product of the vectors corresponding to the new problem record and the document, and wherein the similarity is utilized to identify a document in the corpus that is most similar to the new problem record; and assigning the new problem record to the problem record owner that is assigned to the identified document. 